Xiaohan Chen

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Zhangyang (Atlas) WangXTX Markets & University of Texas at AustinEmail yang diverifikasi di utexas.edu
Jialin LiuUniversity of Central FloridaEmail yang diverifikasi di ucf.edu
Wotao YinDAMO Academy, Alibaba Group USEmail yang diverifikasi di alibaba-inc.com
Tianlong ChenAssistant Professor, CS@UNC Chapel Hill; PostDoc, CSAIL@MIT+BMI@Harvard; Ph.D., ECE@UT AustinEmail yang diverifikasi di cs.unc.edu
Yingyan (Celine) LinAssociate Professor, Georgia Institute of TechnologyEmail yang diverifikasi di gatech.edu
Yue WangMagna InternationalEmail yang diverifikasi di magna.com
Haoran YouGeorgia Institute of TechnologyEmail yang diverifikasi di gatech.edu
Yang (Katie) ZhaoAssistant Professor, University of Minnesota, Twin CitiesEmail yang diverifikasi di umn.edu
Yonggan FuGeorgia Institute of TechnologyEmail yang diverifikasi di gatech.edu
Howard HeatonTypal AcademyEmail yang diverifikasi di typal.academy
Ernest K. RyuUniversity of California, Los AngelesEmail yang diverifikasi di math.ucla.edu
Nitin BansalStaff Engineer, Samsung ResearchEmail yang diverifikasi di samsung.com
Richard BaraniukC. Sidney Burrus Professor, Electrical & Computer Engineering, Rice UniversityEmail yang diverifikasi di rice.edu
Zhe GanResearch Scientist, AppleEmail yang diverifikasi di apple.com
Yu ChengThe Chinese University of Hong KongEmail yang diverifikasi di cse.cuhk.edu.hk
Shuohang WangPrincipal Researcher, Microsoft GenAIEmail yang diverifikasi di microsoft.com
Wuyang ChenAssistant Professor, CS@Simon Fraser UniversityEmail yang diverifikasi di sfu.ca
Bobak J. MortazaviComputer Science & Engineering, Texas A&MEmail yang diverifikasi di tamu.edu
Ziyu JiangResearch Assistant, Texas A&M UniversityEmail yang diverifikasi di tamu.edu
Yuan XieChair Professor of Hong Kong University of Science and Technology (HKUST)Email yang diverifikasi di ust.hk

Ikuti

Xiaohan Chen

DAMO Academy, Alibaba Group

Email yang diverifikasi di utexas.edu - Beranda

Sparse Optimization Inverse Problems Learning to Optimize Meta Learning Efficient Deep Learning


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Plug-and-play methods provably converge with properly trained denoisers E Ryu, J Liu, S Wang, X Chen, Z Wang, W Yin International Conference on Machine Learning, 5546-5557, 2019	432	2019
Can we gain more from orthogonality regularizations in training deep networks? N Bansal, X Chen, Z Wang Advances in Neural Information Processing Systems 31, 2018	383	2018
Drawing early-bird tickets: Towards more efficient training of deep networks H You, C Li, P Xu, Y Fu, Y Wang, X Chen, RG Baraniuk, Z Wang, Y Lin arXiv preprint arXiv:1909.11957, 2019	292	2019
Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds X Chen, J Liu, Z Wang, W Yin Advances in Neural Information Processing Systems 31, 2018	288	2018
Learning to optimize: A primer and a benchmark T Chen, X Chen, W Chen, H Heaton, J Liu, Z Wang, W Yin Journal of Machine Learning Research 23 (189), 1-59, 2022	256	2022
ALISTA: Analytic weights are as good as learned weights in LISTA J Liu, X Chen International Conference on Learning Representations (ICLR), 2019	222	2019
More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity S Liu, T Chen, X Chen, X Chen, Q Xiao, B Wu, T Kärkkäinen, ... arXiv preprint arXiv:2207.03620, 2022	208	2022
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration S Liu, T Chen, X Chen, Z Atashgahi, L Yin, H Kou, L Shen, M Pechenizkiy, ... Neural Information Processing Systems 2021, 2021	134	2021
The unreasonable effectiveness of random pruning: Return of the most naive baseline for sparse training S Liu, T Chen, X Chen, L Shen, DC Mocanu, Z Wang, M Pechenizkiy arXiv preprint arXiv:2202.02643, 2022	123	2022
Federated dynamic sparse training: Computing less, communicating less, yet learning better S Bibikar, H Vikalo, Z Wang, X Chen Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6080-6088, 2022	108	2022
E2-train: Training state-of-the-art cnns with over 80% energy savings Y Wang, Z Jiang, X Chen, P Xu, Y Zhao, Y Lin, Z Wang Advances in Neural Information Processing Systems 32, 2019	108	2019
Shiftaddnet: A hardware-inspired deep network H You, X Chen, Y Zhang, C Li, S Li, Z Liu, Z Wang, Y Lin Advances in Neural Information Processing Systems 33, 2771-2783, 2020	96	2020
Earlybert: Efficient bert training via early-bird lottery tickets X Chen, Y Cheng, S Wang, Z Gan, Z Wang, J Liu arXiv preprint arXiv:2101.00063, 2020	94	2020
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? X Ma, G Yuan, X Shen, T Chen, X Chen, X Chen, N Liu, M Qin, S Liu, ... Neural Information Processing Systems 2021, 2021	66	2021
Deep ensembling with no overhead for either training or testing: The all-round blessings of dynamic sparsity S Liu, T Chen, Z Atashgahi, X Chen, G Sokar, E Mocanu, M Pechenizkiy, ... arXiv preprint arXiv:2106.14568, 2021	59	2021
Smartexchange: Trading higher-cost memory storage/access for lower-cost computation Y Zhao, X Chen, Y Wang, C Li, H You, Y Fu, Y Xie, Z Wang, Y Lin 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020	54	2020
Learning a minimax optimizer: A pilot study J Shen, X Chen, H Heaton, T Chen, J Liu, W Yin, Z Wang International Conference on Learning Representations, 2020	32	2020
Safeguarded learned convex optimization H Heaton, X Chen, Z Wang, W Yin Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7848-7855, 2023	31	2023
The Elastic Lottery Ticket Hypothesis X Chen, Y Cheng, S Wang, Z Gan, J Liu, Z Wang Neural Information Processing Systems 2021, 2021	31	2021
Hyperparameter Tuning is All You Need for LISTA X Chen, J Liu, Z Wang, W Yin Neural Information Processing Systems 2021, 2021	29	2021

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